SoroushVahidi/scidocs huggingface dataset:
Computer Architecture with Associative Processor Replacing Last-Level Cache and SIMD Accelerator
(2015)

This study presents a computer architecture, where a last-level cache and a SIMD accelerator are replaced by an associative processor. Associative processor combines data storage and data processing, and functions as a massively parallel SIMD processor and a memory at the same time. An analytic performance model of this computer architecture is introduced. Comparative analysis supported by cycle-accurate simulation and emulation shows that this architecture may outperform a conventional computer architecture comprising a SIMD coprocessor and a shared last-level cache while consuming less power.
Improved image-based classification of Alzheimer ’ s disease using multimodality brain imaging data
(2015)

Purpose Amyloid-PET is an accurate marker of Alzheimer's disease. However, this technique is invasive and not widely available. Here we investigated whether other neuroimaging techniques can yield similar diagnostic accuracy as PET imaging. This proof of concept study presents two approaches of analyzing multi-modality combinations of metabolic, functional and anatomical brain imaging data using kernel-based pattern recognition techniques. Methods Input images were taken from a systematically collected database of previous studies. Data included [ 18 F]FDG-PET, rs-fMRI and structural MR data of 15 controls and 25 AD patients. [ 11 C]PIB was used as reference test for classification of AD using imaging data. From rs-fMRI data, both eigenvector centrality (EC) images and default mode network (DMN) regression maps were calculated. Structural MR data was used to create gray matter (GM) maps. Intermediate results were combined and analyzed with two types of existing pattern classification software; 1) using spatial concatenation of voxel data and, 2) a regional multi-kernel approach.
What to expect from teleconferencing.
(1984)

Like other marvels of the electronic age, teleconferencing has been both oversold and underused. Though it has many potential uses, what managers know, or think they know, about it is generally based on misconceptions. Rather than relying only on vendors of teleconferencing, potential purchasers should first decide what their communication needs are, then choose the system that suits them best. These authors explain the new teleconferencing options and give guidelines for adapting them to a particular company.
Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values
(2008)

We explore contextual and dispositional correlates of the motivation to contribute to open source initiatives. We examine how the context of the open source project, and the personal values of contributors, are related to the types of motivations for contributing. A web-based survey was administered to 300 contributors in two prominent open source contexts: software and content. As hypothesized, software contributors placed a greater emphasis on reputation-gaining and self-development motivations, compared with content contributors, who placed a greater emphasis on altruistic motives. Furthermore, the hypothesized relationships were found between contributors’ personal values and their motivations for contributing. 2007 Elsevier Ltd. All rights reserved.
On the cognitive process of human problem solving
(2010)

One of the fundamental human cognitive processes is problem solving. As a higher-layer cognitive process, problem solving interacts with many other cognitive processes such as abstraction, searching, learning, decision making, inference, analysis, and synthesis on the basis of internal knowledge representation by the object–attribute-relation (OAR) model. Problem solving is a cognitive process of the brain that searches a solution for a given problem or finds a path to reach a given goal. When a problem object is identified, problem solving can be perceived as a search process in the memory space for finding a relationship between a set of solution goals and a set of alternative paths. This paper presents both a cognitive model and a mathematical model of the problem solving process. The cognitive structures of the brain and the mechanisms of internal knowledge representation behind the cognitive process of problem solving are explained. The cognitive process is formally described using real-time process algebra (RTPA) and concept algebra. This work is a part of the cognitive computing project that designed to reveal and simulate the fundamental mechanisms and processes of the brain according to Wang’s layered reference model of the brain (LRMB), which is expected to lead to the development of future generation methodologies for cognitive computing and novel cognitive computers that are capable of think, learn, and perceive. 2008 Published by Elsevier B.V.
Move Evaluation in Go Using Deep Convolutional Neural Networks
(2015)

The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional-search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates two million positions per move.
Summarizability in OLAP and Statistical Data Bases
(1997)

Summarizability of OLAP and Statistical Databases is an a extremely important property because violating this condition can lead to erroneous conclusions and decisions. In this paper we explore the conditions for summarizability. We introduce a framework for specifying precisely the context in which statistical objects are defined. We use a three step process to define normalized statistical objects. Using this framework, we identify three necessary conditions for summarizability. We provided specific tests for each of the conditions that can be verified either from semantic knowledge, or by checking the statistical database itself. We also provide the reasoning for our belief that these three summarizability conditions are sufficient as well.
Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization
(2018)

This paper presents our design and experience with Andromeda, Google Cloud Platform’s network virtualization stack. Our production deployment poses several challenging requirements, including performance isolation among customer virtual networks, scalability, rapid provisioning of large numbers of virtual hosts, bandwidth and latency largely indistinguishable from the underlying hardware, and high feature velocity combined with high availability. Andromeda is designed around a flexible hierarchy of flow processing paths. Flows are mapped to a programming path dynamically based on feature and performance requirements. We introduce the Hoverboard programming model, which uses gateways for the long tail of low bandwidth flows, and enables the control plane to program network connectivity for tens of thousands of VMs in seconds. The on-host dataplane is based around a highperformance OS bypass software packet processing path. CPU-intensive per packet operations with higher latency targets are executed on coprocessor threads. This architecture allows Andromeda to decouple feature growth from fast path performance, as many features can be implemented solely on the coprocessor path. We demonstrate that the Andromeda datapath achieves performance that is competitive with hardware while maintaining the flexibility and velocity of a software-based architecture.
Microwave antenna applications of metasurfaces
(2015)

This paper reviews the past and current research progress in microwave antenna applications using 2D planar metamaterials or metasurfaces. Firstly, we revisit the definitions of metamaterial and metasurface, and the applications of the ultra-thin metasurfaces for low-profile printed antennas in microwave frequency regime, for the purposes of size reduction and performance enhancement. Then we summarize the different types of metasurfaces and their applications. Finally, we provide the future lookout into microwave antenna and EM related applications using metasurfaces.
A Modified Node2vec Method for Disappearing Link Prediction
(2017)

Disappearing link prediction aims to predict the possibility of the links disappearing in the future. This paper describes the disappearing link prediction problem in scientific collaboration networks based on network embedding. We propose a novel network embedding method called TDL2vec, which is an extension of node2vec algorithm. TDL2vec generates the link embdeddings considering with the time factor. In this paper, the disappearing link prediction problem is considered as a binary classification problem, and support vector machine (SVM) is used as the classifier after link embedding. To evaluate the performance in disappearing link prediction, this paper tests the proposed method and several baseline methods on a real-world network. The experimental results show that TDL2vec achieves better performance than other baselines.
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
(2019)

Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable structures as they are commonly over-smoothed and/or blurry. This paper develops a new approach for image inpainting that does a better job of reproducing filled regions exhibiting fine details. We propose a two-stage adversarial model EdgeConnect that comprises of an edge generator followed by an image completion network. The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori. We evaluate our model end-to-end over the publicly available datasets CelebA, Places2, and Paris StreetView, and show that it outperforms current state-ofthe-art techniques quantitatively and qualitatively.
Learning Temporal Transformations From Time-Lapse Videos
(2016)

Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.
Large-Scale Training of Shadow Detectors with Noisily-Annotated Shadow Examples
(2016)

This paper introduces training of shadow detectors under the large-scale dataset paradigm. This was previously impossible due to the high cost of precise shadow annotation. Instead, we advocate the use of quickly but imperfectly labeled images. Our novel label recovery method automatically corrects a portion of the erroneous annotations such that the trained classifiers perform at state-of-the-art level. We apply our method to improve the accuracy of the labels of a new dataset that is 20 times larger than existing datasets and contains a large variety of scenes and image types. Naturally, such a large dataset is appropriate for training deep learning methods. Thus, we propose a semantic-aware patch level Convolutional Neural Network architecture that efficiently trains on patch level shadow examples while incorporating image level semantic information. This means that the detected shadow patches are refined based on image semantics. Our proposed pipeline can be a useful baseline for future advances in shadow detection.
Semantic Segmentation using Adversarial Networks
(2016)

Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.
Exploring Time-Sensitive Variational Bayesian Inference LDA for Social Media Data
(2017)

There is considerable interest among both researchers and the mass public in understanding the topics of discussion on social media as they occur over time. Scholars have thoroughly analysed samplingbased topic modelling approaches for various text corpora including social media; however, another LDA topic modelling implementation— Variational Bayesian (VB)—has not been well studied, despite its known e ciency and its adaptability to the volume and dynamics of social media data. In this paper, we examine the performance of the VB-based topic modelling approach for producing coherent topics, and further, we extend the VB approach by proposing a novel time-sensitive Variational Bayesian implementation, denoted as TVB. Our newly proposed TVB approach incorporates time so as to increase the quality of the generated topics. Using a Twitter dataset covering 8 events, our empirical results show that the coherence of the topics in our TVB model is improved by the integration of time. In particular, through a user study, we find that our TVB approach generates less mixed topics than state-of-the-art topic modelling approaches. Moreover, our proposed TVB approach can more accurately estimate topical trends, making it particularly suitable to assist end-users in tracking emerging topics on social media.
The effect of experimental presentation of thin media images on body satisfaction: a meta-analytic review.
(2002)

OBJECTIVE
The effect of experimental manipulations of the thin beauty ideal, as portrayed in the mass media, on female body image was evaluated using meta-analysis.


METHOD
Data from 25 studies (43 effect sizes) were used to examine the main effect of mass media images of the slender ideal, as well as the moderating effects of pre-existing body image problems, the age of the participants, the number of stimulus presentations, and the type of research design.


RESULTS
Body image was significantly more negative after viewing thin media images than after viewing images of either average size models, plus size models, or inanimate objects. This effect was stronger for between-subjects designs, participants less than 19 years of age, and for participants who are vulnerable to activation of a thinness schema.


CONCLUSION
Results support the sociocultural perspective that mass media promulgate a slender ideal that elicits body dissatisfaction. Implications for prevention and research on social comparison processes are considered.
Motivated recruitment of autobiographical memories.
(1990)

We hypothesized that people motivated to believe that they possess a given trait search for autobiographical memories that reflect that trait, so as to justify their desired self-view. We led subjects to believe that either extraversion or introversion was desirable, and obtained convergent evidence from open-ended memory-listing tasks as well as from reaction-time tasks measuring the speed with which memories could be generated that this manipulation enhanced the accessibility of memories reflecting the desired trait. If people rely on their memories to construct desired self-concepts, motivated changes in self-concepts should be constrained by the content of available memories. Our final study demonstrates such constraints.
Evading android runtime analysis via sandbox detection
(2014)

The large amounts of malware, and its diversity, have made it necessary for the security community to use automated dynamic analysis systems. These systems often rely on virtualization or emulation, and have recently started to be available to process mobile malware. Conversely, malware authors seek to detect such systems and evade analysis. In this paper, we present techniques for detecting Android runtime analysis systems. Our techniques are classified into four broad classes showing the ability to detect systems based on differences in behavior, performance, hardware and software components, and those resulting from analysis system design choices. We also evaluate our techniques against current publicly accessible systems, all of which are easily identified and can therefore be hindered by a motivated adversary. Our results show some fundamental limitations in the viability of dynamic mobile malware analysis platforms purely based on virtualization.
Part Detector Discovery in Deep Convolutional Neural Networks
(2014)

Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called “part detector discovery” (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. This allows us not just to obtain excellent performance on the CUB2002011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at http://www.inf-cv.uni-jena.de/part_ discovery and https://github.com/cvjena/PartDetectorDisovery.
Flexible dual TCP/UDP streaming for H.264 HD video over WLANs
(2013)

High Definition video streaming over WLANs faces many challenges because video data requires not only data integrity but also frames have strict playout deadline. Traditional streaming methods that rely solely on either UDP or TCP have difficulties meeting both requirements because UDP incurs packet loss while TCP incurs delay. This paper proposed a new streaming method called Flexible Dual-TCP/UDP Streaming Protocol (FDSP) that utilizes the benefit of both UDP and TCP. The FDSP takes advantage of the hierarchical structure of the H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit non-important elements. The proposed FDSP is implemented and validated under different wireless network conditions. Both visual quality and delay results are compared against pure-UDP and pure-TCP streaming methods. Our results show that FDSP effectively achieves a balance between delay and visual quality, thus it has advantage over traditional pure-UDP and pure-TCP methods.
Probabilistic models for discovering e-communities
(2006)

The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology measured by communication frequencies while ignoring the semantic information in SNs. In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs. To simulate the generative models, an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods. Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities.
Ideal Ratio Mask Estimation Using Deep Neural Networks for Monaural Speech Segregation in Noisy Reverberant Conditions
(2017)

Monaural speech segregation is an important problem in robust speech processing and has been formulated as a supervised learning problem. In supervised learning methods, the ideal binary mask (IBM) is usually used as the target because of its simplicity and large speech intelligibility gains. Recently, the ideal ratio mask (IRM) has been found to improve the speech quality over the IBM. However, the IRM was originally defined in anechoic conditions and did not consider the effect of reverberation. In this paper, the IRM is extended to reverberant conditions where the direct sound and early reflections of target speech are regarded as the desired signal. Deep neural networks (DNNs) is employed to estimate the extended IRM in the noisy reverberant conditions. The estimated IRM is then applied to the noisy reverberant mixture for speech segregation. Experimental results show that the estimated IRM provides substantial improvements in speech intelligibility and speech quality over the unprocessed mixture signals under various noisy and reverberant conditions.
Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem
(2012)

The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Traveling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.